Unique ID: WB42

Division: EDU
Issue Date: February 13th 2019
Last modified: February 22nd 2019
Collaborative

Using Big Data to Predict Student Achievement in Low-Income School Settings

Using Big Data to Predict Student Achievement in Low-Income School Settings

SDG: 04 - Quality Education08 - Decent Work & Economic Growth

Accurately predicting student performance early allows mitigating interventions to be effectively designed and applied. Prediction of student achievement is therefore highly valuable to policymakers. This proposal seeks to test whether existing Learning Outcome Predicting Artificial Neural Networks (LOPANNs) can perform with the same degrees of accuracy in lower-income settings as in higher-income settings. Using large data sets from Vietnam and Indonesia, it would determine LOPANNs could reproduce the accuracy they have achieved in the US, Belgium, and Argentina.

Project Sources
Project Sources
Type Of Institution: international organization
Region: East Asia & Pacific
Country Area: Vietnam, Indonesia
Id Country Regional: country
SDG Indicators
SDG Indicators
SDG Comments: 4.1, 4.2, 4.3, 4.4, 4.5, 4.6, 4.7, 4a, 4b, 4c, 8.6
SDG: 04 - Quality Education08 - Decent Work & Economic Growth
Other
Other
Income Level: Lower-middle-income
Timeframe To Produce Indicator: NA
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